Cooperation segmentation method fusing perception information

Abstract

The invention discloses a cooperation segmentation method fusing perception information, which is used for performing joint segmentation on a group of image data set containing a common object, and each image can contain a plurality of common objects. The cooperation segmentation method comprises steps of using perception information like the significance, the repeatability, the space position, etc, which are based on the area as a global bound term to introduce an energy model to define the foreground likelihood, fully playing the important role of the perception information, converting the segmentation problem to the combination optimization problem to solve and using the object structural constraint based on the perception to perform iterative solution. Compared with the similar algorithm, the cooperation segmentation method fusing perception information and the solving method can be applicable to various complex occasions and can effectively segment the object.

Description

technical field [0001] The invention relates to a collaborative segmentation model for fused perceptual information, which is suitable for the fields of target collaborative segmentation of pictures, object segmentation and image recognition in sports pictures, and the like. Background technique [0002] In the field of computer vision, image segmentation, as the basic operation process of image processing, is the basic research topic in the field of computer vision research. Its solution can play a very good role in promoting many other image processing problems such as target classification, recognition, tracking, etc. . In practical applications, medical diagnosis, security monitoring, human-computer interaction, industrial automation and even military guidance are closely related to image segmentation. With the increase of image information, people can easily obtain a large number of pictures containing the same object or objects of the same category, and how to segment...

AI Technical Summary

Problems solved by technology

[0003] 1) The existing step-by-step method cannot form a unified framework for solving. It mainly uses the prior information that multiple images contain the same object or the same type of object in the initialization stage, but does not give full play to the prior information in subsequent algorithms. the role of information;

[0004] 2) The current mainstream algorithms are often sensitive to the initialization of the target. If the accuracy of the initialization is low, the corresponding segmentation effect will be poor;

[0005] 3) The use of some features such as saliency and spatial location information is not enough

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